Abstract
This research proposes an approach to predict equipment condition using OEE performance metrics. Statistical tools are used to correlate measurements of OEE factors and maintenance history from a real database. The results suggesting that there is a correlation between the Time Between Stoppages and the trend degree of the Mean and/or the Standard Deviation (SD) of cycle time. This approach intended to help the predictions of shutdowns for maintenance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abeygunawardane SK, Jirutitijaroen P, Xu H (2013) Adaptive maintenance policies for aging devices using a Markov decision process. IEEE 28(3):3194–3203
Ahmad R, Kamaruddin S (2012) An overview of time-based and condition-based maintenance in industrial application. Comput Ind Eng 63:35–149
Ahmad R, Kamaruddin S (2013) Maintenance decision-making process for a multi-component production unit using output-based maintenance technique: a case study for non-repairable two serial components. Unit Int J Performability Eng 9(3):305–319
Almeanazel OTR (2010) Total productive maintenance review and overall equipment effectiveness measurement. Jordan J Mech Ind Eng 4(4):517–522
Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510
Kumar U, Galar D, Parida A, Stenstrom C, Berges L (2013) Maintenance performance metrics: a state-of-the-art review. J Qual Maintenance Eng 19(3):233–277
Kurscheidt Netto RJ, Santos EAP, Loures ER, Pierezan R (2014) Condition-based maintenance using OEE: an approach to failure probability estimation. In: Proceedings of 7th international conference on production research—Americas 2014, Lima
Maurya MR, Paritosh PK, Rengaswamy R, Venkatasubramanian V (2010) A framework for on-line trend extraction and fault diagnosis. Eng Appl Artif Intell 23(6):950–960
Nakajima S (1988) An introduction to TPM: total productive maintenance. Productivity Press, Portland
Rozinat A, Mans RS, Song M, Van Der Aalst WMP (2009) Discovering simulation models. Inf Syst 34(3):305–327
Santos EAP, De Freitas RL, Deschamps F, De Paula MAB (2008) Proposal of an industrial information system model for automatic performance evaluation. In: IEEE international conference on emerging technologies and factory automation, pp 436–439
Van Der Aalst WMP, Schonenberg MH, Song M (2011) Time prediction based on process mining. Inf Syst 47(2):237–267
Venkatasubramanian V, Rengaswamy R, Kavuri SN, Yin K (2003) A review of process fault detection and diagnosis: part III: process history based methods. Comput Chem Eng 27(3):327–346. Elsevier
Wang W (2012) An overview of the recent advances in delay-time-based maintenance modeling. Reliab Eng Syst Safe 106:165–178
Weber P, Bordbar B, Tino P, Majeed B (2011) A framework for comparing process mining algorithms. In: GCC conference and exhibition (GCC), 2011 IEEE, pp 625–628
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this paper
Cite this paper
Kurscheidt Netto, R.J., Santos, E.A.P., de Freitas Rocha Loures, E., Pierezan, R. (2017). Using Overall Equipment Effectiveness (OEE) to Predict Shutdown Maintenance. In: Amorim, M., Ferreira, C., Vieira Junior, M., Prado, C. (eds) Engineering Systems and Networks. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-45748-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-45748-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45746-8
Online ISBN: 978-3-319-45748-2
eBook Packages: EngineeringEngineering (R0)